Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations44084
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory160.0 B

Variable types

Categorical2
Numeric14

Alerts

alt is highly overall correlated with gtpHigh correlation
cholesterol_ratio is highly overall correlated with hdl and 3 other fieldsHigh correlation
gender is highly overall correlated with height_cm and 3 other fieldsHigh correlation
gtp is highly overall correlated with altHigh correlation
hdl is highly overall correlated with cholesterol_ratio and 1 other fieldsHigh correlation
height_cm is highly overall correlated with gender and 2 other fieldsHigh correlation
hemoglobin is highly overall correlated with gender and 2 other fieldsHigh correlation
ldl is highly overall correlated with cholesterol_ratio and 1 other fieldsHigh correlation
ldl_hdl_ratio is highly overall correlated with cholesterol_ratio and 2 other fieldsHigh correlation
smoking is highly overall correlated with genderHigh correlation
triglyceride is highly overall correlated with cholesterol_ratioHigh correlation
waist_cm is highly overall correlated with weight_kgHigh correlation
weight_kg is highly overall correlated with gender and 3 other fieldsHigh correlation
alt is highly skewed (γ1 = 38.9661536) Skewed

Reproduction

Analysis started2025-04-19 13:14:06.902942
Analysis finished2025-04-19 13:14:17.348920
Duration10.45 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
1
27959 
0
16125 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44084
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Length

2025-04-19T09:14:17.385417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T09:14:17.429465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Most occurring characters

ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

gtp
Real number (ℝ)

High correlation 

Distinct456
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.736934
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:17.480805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q117
median25
Q343
95-th percentile106
Maximum999
Range998
Interquartile range (IQR)26

Descriptive statistics

Standard deviation46.216111
Coefficient of variation (CV)1.1930761
Kurtosis72.304968
Mean38.736934
Median Absolute Deviation (MAD)10
Skewness6.5117151
Sum1707679
Variance2135.929
MonotonicityNot monotonic
2025-04-19T09:14:17.539740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1707
 
3.9%
15 1689
 
3.8%
14 1651
 
3.7%
17 1638
 
3.7%
18 1586
 
3.6%
13 1481
 
3.4%
19 1452
 
3.3%
20 1403
 
3.2%
21 1340
 
3.0%
22 1288
 
2.9%
Other values (446) 28849
65.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 11
 
< 0.1%
6 38
 
0.1%
7 93
 
0.2%
8 188
 
0.4%
9 406
0.9%
10 734
1.7%
ValueCountFrequency (%)
999 1
< 0.1%
961 1
< 0.1%
933 1
< 0.1%
926 1
< 0.1%
910 1
< 0.1%
894 1
< 0.1%
875 1
< 0.1%
873 1
< 0.1%
850 1
< 0.1%
816 1
< 0.1%

hemoglobin
Real number (ℝ)

High correlation 

Distinct142
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.614207
Minimum4.9
Maximum20.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:17.595673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile12.1
Q113.6
median14.8
Q315.7
95-th percentile16.8
Maximum20.9
Range16
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.559347
Coefficient of variation (CV)0.10670076
Kurtosis1.2477973
Mean14.614207
Median Absolute Deviation (MAD)1.1
Skewness-0.66575185
Sum644252.7
Variance2.431563
MonotonicityNot monotonic
2025-04-19T09:14:17.653451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.4 1207
 
2.7%
15 1197
 
2.7%
15.6 1194
 
2.7%
15.3 1191
 
2.7%
15.5 1173
 
2.7%
15.7 1171
 
2.7%
15.2 1118
 
2.5%
14.9 1116
 
2.5%
15.8 1103
 
2.5%
15.1 1102
 
2.5%
Other values (132) 32512
73.8%
ValueCountFrequency (%)
4.9 1
 
< 0.1%
5 2
 
< 0.1%
5.5 2
 
< 0.1%
5.8 2
 
< 0.1%
5.9 1
 
< 0.1%
6.1 1
 
< 0.1%
6.2 1
 
< 0.1%
6.3 5
< 0.1%
6.4 1
 
< 0.1%
6.6 3
< 0.1%
ValueCountFrequency (%)
20.9 1
 
< 0.1%
20.4 1
 
< 0.1%
20 1
 
< 0.1%
19.7 1
 
< 0.1%
19.6 2
 
< 0.1%
19.5 1
 
< 0.1%
19.3 2
 
< 0.1%
19.2 2
 
< 0.1%
19.1 6
< 0.1%
19 4
< 0.1%

height_cm
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.64103
Minimum130
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:17.700226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile150
Q1160
median165
Q3170
95-th percentile180
Maximum190
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1898346
Coefficient of variation (CV)0.055817404
Kurtosis-0.6170455
Mean164.64103
Median Absolute Deviation (MAD)5
Skewness-0.14045689
Sum7258035
Variance84.45306
MonotonicityNot monotonic
2025-04-19T09:14:17.742783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
170 9017
20.5%
165 7844
17.8%
160 7079
16.1%
175 6342
14.4%
155 6035
13.7%
150 3588
 
8.1%
180 2487
 
5.6%
145 965
 
2.2%
185 534
 
1.2%
140 161
 
0.4%
Other values (3) 32
 
0.1%
ValueCountFrequency (%)
130 1
 
< 0.1%
135 3
 
< 0.1%
140 161
 
0.4%
145 965
 
2.2%
150 3588
 
8.1%
155 6035
13.7%
160 7079
16.1%
165 7844
17.8%
170 9017
20.5%
175 6342
14.4%
ValueCountFrequency (%)
190 28
 
0.1%
185 534
 
1.2%
180 2487
 
5.6%
175 6342
14.4%
170 9017
20.5%
165 7844
17.8%
160 7079
16.1%
155 6035
13.7%
150 3588
 
8.1%
145 965
 
2.2%

triglyceride
Real number (ℝ)

High correlation 

Distinct390
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.77116
Minimum8
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:17.796256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q174
median107
Q3159
95-th percentile274
Maximum999
Range991
Interquartile range (IQR)85

Descriptive statistics

Standard deviation70.655231
Coefficient of variation (CV)0.56177608
Kurtosis2.0593309
Mean125.77116
Median Absolute Deviation (MAD)39
Skewness1.3220634
Sum5544496
Variance4992.1617
MonotonicityNot monotonic
2025-04-19T09:14:17.854687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 418
 
0.9%
79 393
 
0.9%
82 393
 
0.9%
83 386
 
0.9%
80 378
 
0.9%
66 374
 
0.8%
69 369
 
0.8%
85 369
 
0.8%
78 368
 
0.8%
67 365
 
0.8%
Other values (380) 40271
91.4%
ValueCountFrequency (%)
8 1
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
16 4
 
< 0.1%
19 2
 
< 0.1%
20 7
< 0.1%
21 6
< 0.1%
22 6
< 0.1%
23 8
< 0.1%
24 12
< 0.1%
ValueCountFrequency (%)
999 1
 
< 0.1%
548 1
 
< 0.1%
466 1
 
< 0.1%
432 1
 
< 0.1%
405 1
 
< 0.1%
399 14
< 0.1%
398 8
< 0.1%
397 14
< 0.1%
396 5
 
< 0.1%
395 8
< 0.1%

serum_creatinine
Real number (ℝ)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88552536
Minimum0.1
Maximum11.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:17.906416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.8
median0.9
Q31
95-th percentile1.2
Maximum11.6
Range11.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.22555141
Coefficient of variation (CV)0.25470914
Kurtosis412.4565
Mean0.88552536
Median Absolute Deviation (MAD)0.1
Skewness10.633209
Sum39037.5
Variance0.050873437
MonotonicityNot monotonic
2025-04-19T09:14:17.960227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.9 8963
20.3%
0.8 8319
18.9%
1 7681
17.4%
0.7 5957
13.5%
1.1 4871
11.0%
0.6 3530
 
8.0%
1.2 2312
 
5.2%
0.5 1181
 
2.7%
1.3 702
 
1.6%
1.4 225
 
0.5%
Other values (28) 343
 
0.8%
ValueCountFrequency (%)
0.1 19
 
< 0.1%
0.2 2
 
< 0.1%
0.3 9
 
< 0.1%
0.4 160
 
0.4%
0.5 1181
 
2.7%
0.6 3530
 
8.0%
0.7 5957
13.5%
0.8 8319
18.9%
0.9 8963
20.3%
1 7681
17.4%
ValueCountFrequency (%)
11.6 1
< 0.1%
10.3 1
< 0.1%
10 2
< 0.1%
9.9 1
< 0.1%
7.5 1
< 0.1%
7.4 1
< 0.1%
6.4 1
< 0.1%
5.9 1
< 0.1%
5 1
< 0.1%
3.4 2
< 0.1%

alt
Real number (ℝ)

High correlation  Skewed 

Distinct229
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.469966
Minimum1
Maximum2914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:18.015160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median21
Q330
95-th percentile60
Maximum2914
Range2913
Interquartile range (IQR)15

Descriptive statistics

Standard deviation28.486264
Coefficient of variation (CV)1.076173
Kurtosis3107.6768
Mean26.469966
Median Absolute Deviation (MAD)7
Skewness38.966154
Sum1166902
Variance811.46726
MonotonicityNot monotonic
2025-04-19T09:14:18.071358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2159
 
4.9%
16 2103
 
4.8%
17 2095
 
4.8%
14 2046
 
4.6%
18 2038
 
4.6%
13 1878
 
4.3%
19 1854
 
4.2%
12 1731
 
3.9%
20 1724
 
3.9%
21 1646
 
3.7%
Other values (219) 24810
56.3%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 1
 
< 0.1%
3 4
 
< 0.1%
4 18
 
< 0.1%
5 41
 
0.1%
6 96
 
0.2%
7 212
 
0.5%
8 402
 
0.9%
9 676
1.5%
10 1087
2.5%
ValueCountFrequency (%)
2914 1
< 0.1%
1783 1
< 0.1%
1504 1
< 0.1%
1400 1
< 0.1%
1155 1
< 0.1%
745 1
< 0.1%
740 1
< 0.1%
713 1
< 0.1%
610 1
< 0.1%
577 1
< 0.1%

weight_kg
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.759573
Minimum30
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:18.122679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile45
Q155
median65
Q375
95-th percentile90
Maximum135
Range105
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.681717
Coefficient of variation (CV)0.19284976
Kurtosis0.21549653
Mean65.759573
Median Absolute Deviation (MAD)10
Skewness0.50221859
Sum2898945
Variance160.82596
MonotonicityNot monotonic
2025-04-19T09:14:18.172176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
65 6551
14.9%
60 6441
14.6%
70 6152
14.0%
55 5834
13.2%
75 4800
10.9%
50 4401
10.0%
80 3235
7.3%
85 2019
 
4.6%
45 1878
 
4.3%
90 1159
 
2.6%
Other values (12) 1614
 
3.7%
ValueCountFrequency (%)
30 5
 
< 0.1%
35 30
 
0.1%
40 375
 
0.9%
45 1878
 
4.3%
50 4401
10.0%
55 5834
13.2%
60 6441
14.6%
65 6551
14.9%
70 6152
14.0%
75 4800
10.9%
ValueCountFrequency (%)
135 1
 
< 0.1%
130 2
 
< 0.1%
125 6
 
< 0.1%
120 15
 
< 0.1%
115 25
 
0.1%
110 67
 
0.2%
105 146
 
0.3%
100 333
 
0.8%
95 609
1.4%
90 1159
2.6%

age
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.178273
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:18.215949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q140
median40
Q355
95-th percentile65
Maximum85
Range65
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.056772
Coefficient of variation (CV)0.27291179
Kurtosis-0.16514734
Mean44.178273
Median Absolute Deviation (MAD)10
Skewness0.25782831
Sum1947555
Variance145.36574
MonotonicityNot monotonic
2025-04-19T09:14:18.261297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
40 11964
27.1%
45 5615
12.7%
60 4877
11.1%
50 4381
 
9.9%
55 4023
 
9.1%
35 3555
 
8.1%
30 3180
 
7.2%
25 2791
 
6.3%
20 1289
 
2.9%
65 1061
 
2.4%
Other values (4) 1348
 
3.1%
ValueCountFrequency (%)
20 1289
 
2.9%
25 2791
 
6.3%
30 3180
 
7.2%
35 3555
 
8.1%
40 11964
27.1%
45 5615
12.7%
50 4381
 
9.9%
55 4023
 
9.1%
60 4877
11.1%
65 1061
 
2.4%
ValueCountFrequency (%)
85 13
 
< 0.1%
80 208
 
0.5%
75 481
 
1.1%
70 646
 
1.5%
65 1061
 
2.4%
60 4877
11.1%
55 4023
 
9.1%
50 4381
 
9.9%
45 5615
12.7%
40 11964
27.1%

waist_cm
Real number (ℝ)

High correlation 

Distinct554
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.962905
Minimum51
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:18.314174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile67
Q176
median82
Q388
95-th percentile97.3
Maximum129
Range78
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.178336
Coefficient of variation (CV)0.11198159
Kurtosis0.065347716
Mean81.962905
Median Absolute Deviation (MAD)6
Skewness0.21262232
Sum3613252.7
Variance84.241852
MonotonicityNot monotonic
2025-04-19T09:14:18.377525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 1520
 
3.4%
82 1419
 
3.2%
81 1361
 
3.1%
84 1355
 
3.1%
78 1322
 
3.0%
86 1305
 
3.0%
85 1288
 
2.9%
83 1261
 
2.9%
79 1211
 
2.7%
76 1201
 
2.7%
Other values (544) 30841
70.0%
ValueCountFrequency (%)
51 1
 
< 0.1%
53 1
 
< 0.1%
54 2
 
< 0.1%
55 3
< 0.1%
56 5
< 0.1%
56.2 2
 
< 0.1%
56.4 1
 
< 0.1%
56.6 1
 
< 0.1%
57 7
< 0.1%
57.2 1
 
< 0.1%
ValueCountFrequency (%)
129 1
 
< 0.1%
127.7 1
 
< 0.1%
127 1
 
< 0.1%
124 1
 
< 0.1%
123 1
 
< 0.1%
121 3
< 0.1%
120.9 1
 
< 0.1%
120 2
< 0.1%
119 1
 
< 0.1%
118.5 1
 
< 0.1%

bp_ratio
Real number (ℝ)

Distinct2179
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6069567
Minimum1.1578947
Maximum2.6078431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:18.443334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.1578947
5-th percentile1.4074613
Q11.5128205
median1.5890411
Q31.6857143
95-th percentile1.8571429
Maximum2.6078431
Range1.4499484
Interquartile range (IQR)0.17289377

Descriptive statistics

Standard deviation0.13834106
Coefficient of variation (CV)0.086088855
Kurtosis1.5719666
Mean1.6069567
Median Absolute Deviation (MAD)0.085377509
Skewness0.75097256
Sum70841.08
Variance0.01913825
MonotonicityNot monotonic
2025-04-19T09:14:18.504878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 1921
 
4.4%
1.571428571 1508
 
3.4%
1.625 1138
 
2.6%
1.666666667 1047
 
2.4%
1.512820513 762
 
1.7%
1.6 544
 
1.2%
1.714285714 520
 
1.2%
1.428571429 380
 
0.9%
1.555555556 363
 
0.8%
1.75 342
 
0.8%
Other values (2169) 35559
80.7%
ValueCountFrequency (%)
1.157894737 1
 
< 0.1%
1.162790698 1
 
< 0.1%
1.163934426 1
 
< 0.1%
1.170212766 1
 
< 0.1%
1.172727273 1
 
< 0.1%
1.181818182 1
 
< 0.1%
1.188235294 1
 
< 0.1%
1.195402299 1
 
< 0.1%
1.2 4
< 0.1%
1.204819277 1
 
< 0.1%
ValueCountFrequency (%)
2.607843137 1
< 0.1%
2.565217391 1
< 0.1%
2.537037037 1
< 0.1%
2.5 2
< 0.1%
2.476190476 1
< 0.1%
2.470588235 1
< 0.1%
2.468085106 1
< 0.1%
2.428571429 1
< 0.1%
2.411764706 1
< 0.1%
2.409836066 1
< 0.1%

cholesterol_ratio
Real number (ℝ)

High correlation 

Distinct7720
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6208839
Minimum0.38349515
Maximum52.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:18.567590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.38349515
5-th percentile2.2068966
Q12.859375
median3.4827586
Q34.2364833
95-th percentile5.4790308
Maximum52.5
Range52.116505
Interquartile range (IQR)1.3771083

Descriptive statistics

Standard deviation1.072312
Coefficient of variation (CV)0.29614647
Kurtosis169.67898
Mean3.6208839
Median Absolute Deviation (MAD)0.67868438
Skewness4.5195863
Sum159623.05
Variance1.149853
MonotonicityNot monotonic
2025-04-19T09:14:18.631574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 290
 
0.7%
4 273
 
0.6%
3.5 184
 
0.4%
5 128
 
0.3%
3.666666667 116
 
0.3%
3.333333333 111
 
0.3%
4.5 96
 
0.2%
2.5 83
 
0.2%
3.25 71
 
0.2%
2.666666667 70
 
0.2%
Other values (7710) 42662
96.8%
ValueCountFrequency (%)
0.3834951456 1
< 0.1%
0.5598885794 1
< 0.1%
1.12244898 1
< 0.1%
1.213483146 1
< 0.1%
1.265060241 1
< 0.1%
1.293478261 1
< 0.1%
1.295238095 1
< 0.1%
1.3 1
< 0.1%
1.326086957 1
< 0.1%
1.340425532 1
< 0.1%
ValueCountFrequency (%)
52.5 1
< 0.1%
48.75 1
< 0.1%
16.35714286 1
< 0.1%
10.42857143 1
< 0.1%
9.897435897 1
< 0.1%
9.888888889 1
< 0.1%
9.822222222 1
< 0.1%
9.612903226 1
< 0.1%
9.333333333 1
< 0.1%
9.24 1
< 0.1%

ldl_hdl_ratio
Real number (ℝ)

High correlation 

Distinct7048
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1372885
Minimum0.02173913
Maximum51.714286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:18.694263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.02173913
5-th percentile0.96551724
Q11.5294118
median2.04
Q32.625
95-th percentile3.6120748
Maximum51.714286
Range51.692547
Interquartile range (IQR)1.0955882

Descriptive statistics

Standard deviation0.97630987
Coefficient of variation (CV)0.45679836
Kurtosis416.55529
Mean2.1372885
Median Absolute Deviation (MAD)0.54490566
Skewness10.899397
Sum94220.226
Variance0.95318096
MonotonicityNot monotonic
2025-04-19T09:14:18.757584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 399
 
0.9%
3 205
 
0.5%
2.5 176
 
0.4%
1.5 171
 
0.4%
1 143
 
0.3%
1.666666667 128
 
0.3%
2.333333333 126
 
0.3%
2.666666667 106
 
0.2%
2.25 104
 
0.2%
1.333333333 100
 
0.2%
Other values (7038) 42426
96.2%
ValueCountFrequency (%)
0.02173913043 1
< 0.1%
0.0243902439 1
< 0.1%
0.08974358974 1
< 0.1%
0.1460674157 1
< 0.1%
0.152173913 1
< 0.1%
0.1523809524 1
< 0.1%
0.16 1
< 0.1%
0.1604938272 1
< 0.1%
0.1643835616 1
< 0.1%
0.1686746988 1
< 0.1%
ValueCountFrequency (%)
51.71428571 1
< 0.1%
45.36585366 1
< 0.1%
42 1
< 0.1%
40.25 1
< 0.1%
33.19148936 1
< 0.1%
27.45098039 1
< 0.1%
25.28301887 1
< 0.1%
25 1
< 0.1%
21.53846154 1
< 0.1%
21.28205128 1
< 0.1%

hdl
Real number (ℝ)

High correlation 

Distinct125
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.318914
Minimum4
Maximum618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:18.813045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile37
Q147
median55
Q366
95-th percentile84
Maximum618
Range614
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.748826
Coefficient of variation (CV)0.25731168
Kurtosis52.199372
Mean57.318914
Median Absolute Deviation (MAD)9
Skewness2.2202778
Sum2526847
Variance217.52788
MonotonicityNot monotonic
2025-04-19T09:14:18.870501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 1325
 
3.0%
50 1324
 
3.0%
54 1309
 
3.0%
56 1309
 
3.0%
53 1295
 
2.9%
55 1281
 
2.9%
49 1265
 
2.9%
52 1263
 
2.9%
47 1261
 
2.9%
48 1257
 
2.9%
Other values (115) 31195
70.8%
ValueCountFrequency (%)
4 2
 
< 0.1%
11 1
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
18 2
 
< 0.1%
21 2
 
< 0.1%
22 2
 
< 0.1%
23 4
 
< 0.1%
24 8
< 0.1%
25 10
< 0.1%
ValueCountFrequency (%)
618 1
 
< 0.1%
359 1
 
< 0.1%
157 1
 
< 0.1%
155 1
 
< 0.1%
148 1
 
< 0.1%
144 1
 
< 0.1%
136 2
< 0.1%
135 2
< 0.1%
133 3
< 0.1%
132 1
 
< 0.1%

ldl
Real number (ℝ)

High correlation 

Distinct283
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.96092
Minimum1
Maximum1860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-19T09:14:18.927380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile63
Q192
median113
Q3136
95-th percentile171
Maximum1860
Range1859
Interquartile range (IQR)44

Descriptive statistics

Standard deviation40.125841
Coefficient of variation (CV)0.34903898
Kurtosis354.74025
Mean114.96092
Median Absolute Deviation (MAD)22
Skewness10.373479
Sum5067937
Variance1610.0831
MonotonicityNot monotonic
2025-04-19T09:14:18.986892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 582
 
1.3%
112 574
 
1.3%
121 559
 
1.3%
111 554
 
1.3%
106 553
 
1.3%
107 550
 
1.2%
116 547
 
1.2%
101 544
 
1.2%
96 541
 
1.2%
114 538
 
1.2%
Other values (273) 38542
87.4%
ValueCountFrequency (%)
1 2
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 4
< 0.1%
13 3
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 5
< 0.1%
ValueCountFrequency (%)
1860 1
< 0.1%
1810 1
< 0.1%
1660 1
< 0.1%
1560 1
< 0.1%
1400 1
< 0.1%
1340 1
< 0.1%
1260 1
< 0.1%
1220 1
< 0.1%
1200 1
< 0.1%
1120 1
< 0.1%

smoking
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
0
27972 
1
16112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44084
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Length

2025-04-19T09:14:19.040433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T09:14:19.083412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Most occurring characters

ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Interactions

2025-04-19T09:14:16.530052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:07.757862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.421855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.075863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.715655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.873327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.501063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.103274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.732957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.369837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.045455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.669893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.305227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.927364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.571150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:07.803286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.464529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.120398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.286217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.920525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.541669image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.145184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.774108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.417019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.091402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.713733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.347641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.969483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.617431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:07.853200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.512812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.166490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.330990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.968340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.586438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.189603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.819077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.464234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.138352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.765663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.395640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.016388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.661192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:07.901522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.561594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.209429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.374242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.017747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.629678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.235407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.863268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.511566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.182804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.815498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.441852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.060570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.702994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:07.947385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.606382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.251616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.414849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.064360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.670945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.280429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.904959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.557126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.226213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.864501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.485417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.101431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.746377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:07.996369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.652457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.294882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.457295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.110144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.713968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.328167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.950764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.602198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.269994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.911972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.531283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.145252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.789177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.041520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.699633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.339833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.504718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.154118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.755450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.375019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.997453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.648201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.314680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.957511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.576477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.187505image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.830761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.085021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.744761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.383230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.549176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.195486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.797009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.419269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.042693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.697885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.357474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.001799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.619760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.228812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.874039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.129113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.791759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.431964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.593871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.238607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.839292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.466495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.086190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.747620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.401509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.045522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.663857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.270855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.920587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.178006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.841774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.482847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.642347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.284624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.885845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.516734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.133866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.797639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.448609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.092000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.711525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.316515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.966655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.229340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.889731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.535731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.689876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.330144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.930183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.563008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.182477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.849699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.494188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.136877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.758449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.361615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:17.010635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.275275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.933420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.580271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.734546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.371344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.971929image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.604524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.229515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.897971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.536639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.177300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.799922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.402765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:17.053320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.322425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.978020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.623785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.778566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.414038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.015599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.646285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.274762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.945833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.579317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.217348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.839476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.444325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:17.096283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:08.373115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.025388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:09.667559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:10.824635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:11.456217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.058308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:12.688749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.322102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:13.995181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:14.623459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.259487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:15.882407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:14:16.486152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-04-19T09:14:19.123886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
agealtbp_ratiocholesterol_ratiogendergtphdlheight_cmhemoglobinldlldl_hdl_ratioserum_creatininesmokingtriglyceridewaist_cmweight_kg
age1.000-0.0740.0660.0240.434-0.0220.020-0.498-0.3170.0650.024-0.1770.1730.030-0.038-0.339
alt-0.0741.000-0.0420.2960.0000.614-0.2660.2660.4160.0900.2250.2330.0000.3510.4550.446
bp_ratio0.066-0.0421.000-0.0590.044-0.0750.021-0.056-0.102-0.048-0.051-0.0450.060-0.060-0.019-0.050
cholesterol_ratio0.0240.296-0.0591.0000.0830.281-0.7490.1480.2680.6280.9470.1910.0810.5770.3840.344
gender0.4340.0000.0440.0831.0000.1330.2480.7780.7350.0290.0110.1130.5080.2300.4340.613
gtp-0.0220.614-0.0750.2810.1331.000-0.2220.2960.4450.0640.1780.2860.1580.4540.4620.433
hdl0.020-0.2660.021-0.7490.248-0.2221.000-0.229-0.272-0.058-0.640-0.2100.143-0.470-0.393-0.378
height_cm-0.4980.266-0.0560.1480.7780.296-0.2291.0000.583-0.0540.1040.4760.4160.1670.3880.698
hemoglobin-0.3170.416-0.1020.2680.7350.445-0.2720.5831.0000.0570.2100.4900.4050.2920.3960.538
ldl0.0650.090-0.0480.6280.0290.064-0.058-0.0540.0571.0000.7730.0450.0120.0930.0940.054
ldl_hdl_ratio0.0240.225-0.0510.9470.0110.178-0.6400.1040.2100.7731.0000.1620.0110.3510.3040.270
serum_creatinine-0.1770.233-0.0450.1910.1130.286-0.2100.4760.4900.0450.1621.0000.0240.1570.2830.419
smoking0.1730.0000.0600.0810.5080.1580.1430.4160.4050.0120.0110.0241.0000.2360.2250.311
triglyceride0.0300.351-0.0600.5770.2300.454-0.4700.1670.2920.0930.3510.1570.2361.0000.3960.345
waist_cm-0.0380.455-0.0190.3840.4340.462-0.3930.3880.3960.0940.3040.2830.2250.3961.0000.806
weight_kg-0.3390.446-0.0500.3440.6130.433-0.3780.6980.5380.0540.2700.4190.3110.3450.8061.000

Missing values

2025-04-19T09:14:17.161597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-19T09:14:17.276276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

gendergtphemoglobinheight_cmtriglycerideserum_creatininealtweight_kgagewaist_cmbp_ratiocholesterol_ratioldl_hdl_ratiohdlldlsmoking
0027.012.915582.00.719.0604081.31.5616442.9452051.72602773.0126.00
1018.012.7160115.00.619.0604081.01.7000004.5714293.02381042.0127.00
2122.015.8170182.01.016.0605580.01.6046514.4000002.74545555.0151.01
3118.014.7165254.01.026.0704088.01.6666677.1555565.02222245.0226.00
4022.012.515574.00.614.0604086.01.6216222.9677421.72580662.0107.00
5133.016.2180199.01.227.0753085.01.6842114.5208332.68750048.0129.00
6139.017.016068.00.727.0604085.51.4146344.1090912.85454555.0157.01
71111.015.0165269.01.371.0904596.01.5937506.5294123.94117634.0134.00
8014.013.715066.00.831.0605085.01.5540544.3750003.10416748.0149.00
9163.016.0175147.00.824.0754589.01.7656254.6046512.93023343.0126.00
gendergtphemoglobinheight_cmtriglycerideserum_creatininealtweight_kgagewaist_cmbp_ratiocholesterol_ratioldl_hdl_ratiohdlldlsmoking
44543114.015.817063.01.013.0652076.01.5714293.2280702.00000057.0114.00
44544010.013.016586.00.816.0454558.01.7733333.0158731.74603263.0110.00
44545132.014.0185131.01.048.0954599.01.4651164.1400002.60000050.0130.00
44546117.015.5160112.01.221.0556077.01.7352943.0000001.65625064.0106.01
44547132.015.5165109.01.117.0706087.01.5897444.0208332.56250048.0123.01
44548151.015.2170168.01.049.0655088.01.5744684.5500002.70000040.0108.00
44549116.015.217560.00.914.0703584.01.4189193.2807022.07017557.0118.00
44550110.013.417558.01.019.0703570.91.6571433.5365852.24390241.092.00
44551140.014.5165139.00.949.09050106.81.6266673.8085112.21276647.0104.01
44552120.011.417551.01.011.0802593.21.6315794.0204082.81632749.0138.00